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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming
Jorge A. Vázquez-Diosdado, Veronica Paul, Keith A. Ellis, et al.
Sensors (2019) Vol. 19, Iss. 14, pp. 3201-3201
Open Access | Times Cited: 50

Showing 1-25 of 50 citing articles:

A systematic literature review on the use of machine learning in precision livestock farming
Rodrigo García, José Aguilar, Mauricio Toro, et al.
Computers and Electronics in Agriculture (2020) Vol. 179, pp. 105826-105826
Open Access | Times Cited: 206
Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data
Lucile Riaboff, L. Shalloo, Alan F. Smeaton, et al.
Computers and Electronics in Agriculture (2021) Vol. 192, pp. 106610-106610
Open Access | Times Cited: 131
Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions
Sarah Morrone, Corrado Dimauro, Filippo Gambella, et al.
Sensors (2022) Vol. 22, Iss. 12, pp. 4319-4319
Open Access | Times Cited: 110
Smart technologies to improve the management and resilience to climate change of livestock housing: a systematic and critical review
Francesco Bordignon, Giorgio Provolo, Elisabetta Riva, et al.
Italian Journal of Animal Science (2025) Vol. 24, Iss. 1, pp. 376-392
Open Access | Times Cited: 3
Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges
Parisa Niloofar, Deena P. Francis, Sanja Lazarova‐Molnar, et al.
Computers and Electronics in Agriculture (2021) Vol. 190, pp. 106406-106406
Open Access | Times Cited: 89
Livestock Management With Unmanned Aerial Vehicles: A Review
Mohammed Ateeq Alanezi, Mohammad Shoaib Shahriar, Md. Bakhtiar Hasan, et al.
IEEE Access (2022) Vol. 10, pp. 45001-45028
Open Access | Times Cited: 62
Deep transfer learning in sheep activity recognition using accelerometer data
Natasa Kleanthous, Abir Hussain, Wasiq Khan, et al.
Expert Systems with Applications (2022) Vol. 207, pp. 117925-117925
Open Access | Times Cited: 48
A survey of machine learning approaches in animal behaviour
Natasa Kleanthous, Abir Hussain, Wasiq Khan, et al.
Neurocomputing (2022) Vol. 491, pp. 442-463
Open Access | Times Cited: 39
Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data
Victor Bloch, Lilli Frondelius, Claudia Arcidiacono, et al.
Sensors (2023) Vol. 23, Iss. 5, pp. 2611-2611
Open Access | Times Cited: 25
Smart technologies for sustainable pasture-based ruminant systems: A review
Sara Marchegiani, G. Gislon, R. Marino, et al.
Smart Agricultural Technology (2025) Vol. 10, pp. 100789-100789
Closed Access | Times Cited: 1
Remote Vital Sensing in Clinical Veterinary Medicine: A Comprehensive Review of Recent Advances, Accomplishments, Challenges, and Future Perspectives
X. Zhao, Ryou Tanaka, Ahmed S. Mandour, et al.
Animals (2025) Vol. 15, Iss. 7, pp. 1033-1033
Open Access | Times Cited: 1
Can accelerometer ear tags identify behavioural changes in sheep associated with parturition?
Eloise S. Fogarty, David L. Swain, G. M. Cronin, et al.
Animal Reproduction Science (2020) Vol. 216, pp. 106345-106345
Closed Access | Times Cited: 54
Machine Learning Algorithms to Classify and Quantify Multiple Behaviours in Dairy Calves Using a Sensor: Moving beyond Classification in Precision Livestock
Charles Carslake, Jorge A. Vázquez-Diosdado, Jasmeet Kaler
Sensors (2020) Vol. 21, Iss. 1, pp. 88-88
Open Access | Times Cited: 52
Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep
Jasmeet Kaler, Jürgen Mitsch, Jorge A. Vázquez-Diosdado, et al.
Royal Society Open Science (2020) Vol. 7, Iss. 1, pp. 190824-190824
Open Access | Times Cited: 41
Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach
Eloise S. Fogarty, David L. Swain, G. M. Cronin, et al.
Animals (2021) Vol. 11, Iss. 2, pp. 303-303
Open Access | Times Cited: 35
Livestock feeding behaviour: A review on automated systems for ruminant monitoring
José O. Chelotti, Luciano S. Martinez-Rau, Mariano Ferrero, et al.
Biosystems Engineering (2024) Vol. 246, pp. 150-177
Open Access | Times Cited: 6
Classifying the posture and activity of ewes and lambs using accelerometers and machine learning on a commercial flock
Emily Price, Joss Langford, Tim W. Fawcett, et al.
Applied Animal Behaviour Science (2022) Vol. 251, pp. 105630-105630
Closed Access | Times Cited: 22
ASAS–NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production
Hector M Menendez, Jameson R Brennan, Charlotte Gaillard, et al.
Journal of Animal Science (2022) Vol. 100, Iss. 6
Open Access | Times Cited: 19
A pipeline with pre-processing options to detect behaviour from accelerometer data using Machine Learning tested on dairy goats.
Sarah Mauny, Joon Kwon, N.C. Friggens, et al.
Peer Community Journal (2025) Vol. 5
Open Access
Exploring the Potential of Machine Learning Algorithms Associated with the Use of Inertial Sensors for Goat Kidding Detection
Pedro Gonçalves, M.R. Marques, A.T. Belo, et al.
Animals (2024) Vol. 14, Iss. 6, pp. 938-938
Open Access | Times Cited: 3
A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach
Federico Divina, Miguel García-Torres, Francisco Gómez-Vela, et al.
AIMS Mathematics (2024) Vol. 9, Iss. 5, pp. 13358-13384
Open Access | Times Cited: 2
Applications of Artificial Intelligence for Heat Stress Management in Ruminant Livestock
Ebenezer Binuni Rebez, Veerasamy Sejian, M. V. Silpa, et al.
Sensors (2024) Vol. 24, Iss. 18, pp. 5890-5890
Open Access | Times Cited: 2
Research on a Low-Cost, Open-Source, and Remote Monitoring Data Collector to Predict Livestock’s Habits Based on Location and Auditory Information: A Case Study from Vietnam
Ha Quang Thinh Ngo, Thanh Phương Nguyễn, Hung T. Nguyen
Agriculture (2020) Vol. 10, Iss. 5, pp. 180-180
Open Access | Times Cited: 12
Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review
Olivier Debauche, Meryem Elmoulat, Saïd Mahmoudi, et al.
Revue d intelligence artificielle (2021) Vol. 35, Iss. 3, pp. 243-253
Open Access | Times Cited: 11
Effects of the environment and animal behavior on nutrient requirements for gestating sows: Future improvements in precision feeding
Charlotte Gaillard, Maëva Durand, Christine Largouët, et al.
Animal Feed Science and Technology (2021) Vol. 279, pp. 115034-115034
Open Access | Times Cited: 11
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